{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-robust-ppo-optimized-tabular-transformer","title":"A Robust PPO-optimized Tabular Transformer Framework for Intrusion Detection in Industrial IoT Systems","arxiv_id":"2505.18234","date":"2025-05-23","proceeding":null,"authors":["Yuanya She"],"abstract":"In this paper, we propose a robust and reinforcement-learning-enhanced network intrusion detection system (NIDS) designed for class-imbalanced and few-shot attack scenarios in Industrial Internet of Things (IIoT) environments. Our model integrates a TabTransformer for effective tabular feature representation with Proximal Policy Optimization (PPO) to optimize classification decisions via policy learning. Evaluated on the TON\\textunderscore IoT benchmark, our method achieves a macro F1-score of 97.73\\% and accuracy of 98.85\\%. Remarkably, even on extremely rare classes like man-in-the-middle (MITM), our model achieves an F1-score of 88.79\\%, showcasing strong robustness and few-shot detection capabilities. Extensive ablation experiments confirm the complementary roles of TabTransformer and PPO in mitigating class imbalance and improving generalization. These results highlight the potential of combining transformer-based tabular learning with reinforcement learning for real-world NIDS applications.","url_abs":"https://arxiv.org/abs/2505.18234v1","url_pdf":"https://arxiv.org/pdf/2505.18234v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-robust-ppo-optimized-tabular-transformer","repo_url":"https://github.com/RussellTNY/PPO-optimized-Tab-Transformer-for-NIDS-on-TON_IoT","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"intrusion-detection","task_name":"Intrusion Detection"},{"task_slug":"network-intrusion-detection","task_name":"Network Intrusion Detection"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"entropy-regularization","method_name":"Entropy Regularization"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"ppo","method_name":"PPO"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"tabtransformer","method_name":"TabTransformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/network-intrusion-detection-on-ton-iot","task":"Network Intrusion Detection","dataset":"ToN_IoT","model":"PPO optimized TabTransformer","rank_in_archive_order":1,"of":1,"metrics":{"Average Class Accuracy":"98.85%"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}